A second-generation trait-based dynamic vegetation model
Zusammenfassung der Projektergebnisse
Dynamic global vegetation models (DGVMs) are the most powerful tool available to ecologists to explore the current and future distribution of vegetation and biogeochemical cycles at large spatial scales. However, one weakness of many DGVMs is that they simulate a fixed number of plant functional types (PFT), where each PFT is defined by a static combination of traits. In reality, traits of PFTs can vary substantially and they are defined by spectra in a multi-dimensional trait space. Plant performance is limited by trade-offs between traits, thus, optimizing a trait to exploit one resource implies a reduced capacity to exploit another resource. We developed a second generation dynamic vegetation model (the aDGVM2) that allows each individual plant to adopt different trait values, constrained only by trade-offs between traits. Genetic optimization algorithms are used to imitate the process of community assembly. That is, individuals with trait values that allow the plant to grow and reproduce in a given environment can inherit their traits to the next generation while other individuals are out-competed. Recombination and mutation of traits further modifies plant traits. This process leads to the assembly of a plant community that is adapted to local site conditions. In the aDGVM2, water transport in plants is simulated using cohesion-tension theory and an Ohm’s law analogy is used to describe water transport through the soil-plant-atmosphere hydraulic continuum. Further, the model is coupled to JSBACH, the land surface scheme of the MPI Hamburg Earth system model. We found that the aDGVM2 simulates different life history strategies such as fast growing pioneer strategies and slow growing climax strategies. These strategies are not predefined but they emerge from the community assembly processes simulated by the aDGVM2. Simulated plant communities respond to environmental gradients (for example rainfall and CO2 ) and disturbance gradients. For instance, the aDGVM2 simulates that under fire suppression trees reduce carbon allocation to bark, because bark protects plants against fire impact. Therefore, carbon allocation to roots increases to improve water uptake. In a case study, we showed that the aDGVM2 can simulate tree biomass, tree cover and biome distribution patterns in Amazonia. The best agreement between model and data was achieved in the presence of fire and when we used variable soil depth rather than a constant soil depth for the entire study region. The aDGVM2 projects that large areas in the study region are bistable (that is, forest is possible but fire establishes an open savanna or grassland state) and that soil depth influences size and range of bistable areas. Simulated plant traits and communities respond to the different environmental conditions in the study area, for example, the model projects more evergreen trees with low specific leaf area in more humid regions an deciduous trees with higher specific leaf area in more arid, seasonal regions. The aDGVM2 deals with functional diversity and competition fundamentally differently from first generation DGVMs. This approach yields novel insights as to how vegetation may respond to climate change or land use at the trait, plant, community and biome level. The aDGVM2 modeling approach can foster collaborations between functional plant biologists and vegetation modelers.
Projektbezogene Publikationen (Auswahl)
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(2012). Fire and fire-adapted vegetation promoted C4 expansion in the late Miocene. New Phytologist, 195, 653-666
Scheiter, S, Higgins, SI, Osborne, CP, Bradshaw, C, Lunt, D, Ripley, BS, Taylor, LL, and Beerling, DJ
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(2013). Next generation dynamic global vegetation models: learning from community ecology. New Phytologist, 198, 957-969
Scheiter, S, Langan, L, and Higgins, SI
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(2014). Progress in DGVMs: a comment on “Impacts of trait variation through observed trait-climate relationships on performance of an earth system model: a conceptual analysis” by Verheijen et al. (2013). Biogeosciences, 11, 4357-4360
Higgins, SI, Langan, L, and Scheiter, S
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(2015). Climate change and long-term fire management impacts on Australian savannas. New Phytologist, 205, 1211-1226
Scheiter, S, Higgins, SI, Beringer, J, and Hutley, LB
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(2015). Climate-biomes, pedo-biomes and pyrobiomes: which world view explains the tropical forest - savanna boundary in south America? European Geosciences Union General Assembly 2015, Vienna, Austria
Langan, L, Higgins, SI, and Scheiter, S
